A future land use simulation method, device, storage medium and terminal device

Abstract

The present invention provides a future land use simulation method, device, storage medium and terminal device, which uses a dynamic vegetation model to simulate the leaf area coverage ratio of natural resources under climate change, and combines the historical land use development suitability simulated by the FLUS model to obtain the future land use demand. The present invention combines the dynamic vegetation model and the FLUS model, clearly simulates the impacts of human activities and natural climate on land use change, thereby improving the effectiveness of simulating future land use, so as to provide more accurate data to help researchers conduct research in the field of geographic information science.

Description

Technical Field

[0001] The present invention relates to the technical field of geographic information science and technology, and particularly relates to a future land use simulation method, device, storage medium and terminal device. Background Art

[0002] In the research of geographic information science and technology, the Cellular Automata (CA) is usually used to simulate land use. In the CA model, establishing the relationship between historical land use changes and natural environment and socio-economic factors, and then mining land use conversion rules to obtain the development potential of land use is the core operation of the CA model. Common land use rule mining methods are mined through logistic regression, artificial neural network, support vector machine, random forest, convolutional neural network, etc. However, due to the lack of effective methods, these traditional CA models do not consider the impact of future climate change on land use change. Therefore, when traditional CA models simulate land use change patterns under climate change, the accuracy is not high.

[0003] The dynamic vegetation model is used to simulate the future vegetation distribution under climate change conditions. This model is a mechanism model integrating vegetation biogeography and biochemistry models. However, since the dynamic vegetation model does not consider the influencing factors of human activities, it cannot simulate land use types involving humans such as cultivated land and cities. And the FLUS model has high accuracy in the simulation research of land use change at the regional scale and even the global scale due to the introduction of an adaptive inertia mechanism on the basis of the CA model, and it is one of the most widely used CA models. However, this model does not consider the land use change pattern under climate change.

[0004] Therefore, in the technical field of geographic information science and technology, a new research method is needed to combine human activities and natural climate to simulate land use change, so as to improve the effectiveness of future land use simulation. Summary of the Invention

[0005] Embodiments of the present invention provide a future land use simulation method, device, storage medium and terminal device, which improve the effectiveness of future land use simulation by combining the dynamic vegetation model and the FLUS model.

[0006] To solve the above problems, an embodiment of the present invention provides a future land use simulation method, including:

[0007] Obtain historical land use data and spatial driving factor data, and input them into the FLUS model to obtain first development suitability data; wherein, the first development suitability data includes historical development suitability of forest land, historical development suitability of grassland and historical development suitability of bare land;

[0008] Obtain natural data and input it into the dynamic vegetation model to obtain the leaf area coverage ratio of natural vegetation; wherein, the natural data includes climate data and natural background data, and the leaf area coverage ratio of the natural vegetation includes the forest land leaf area coverage ratio, the grassland leaf area coverage ratio, and the bare land leaf area coverage ratio;

[0009] According to the leaf area coverage ratio, perform weighted calculation on the first development suitability data through a preset algorithm to obtain the second development suitability data;

[0010] According to the second development suitability data, use a preset conversion rule to perform iterative conversion of the target land use type to obtain the land use type corresponding to different grids, and stop the iteration and output the future land use demand until the number of land use types obtained reaches a preset value.

[0011] As an improvement of the above solution, the obtaining of the land historical use data and the spatial driving factor data and inputting them into the FLUS model to obtain the first development suitability data is specifically as follows:

[0012] Collect the land historical use data within a preset time, and collect human impact factors, terrain factors, soil factors, and climate factors to obtain the spatial driving factor data;

[0013] According to the land historical use data and the spatial driving factor data, use the method of stratified random sampling to obtain samples and input them into the FLUS model for training, testing, and verification to obtain the first development suitability data.

[0014] As an improvement of the above solution, the collecting of human impact factors, terrain factors, soil factors, and climate factors to obtain the spatial driving factor data is specifically as follows:

[0015] Calculate the first distance data of each pixel point in a preset area to the city center and the second distance data of each pixel point to the road to obtain the distance driving factor layer;

[0016] Collect elevation data and calculate the slope data of each pixel point in the area to obtain the slope driving factor layer;

[0017] According to the population raster data, soil raster data, and climate raster data, project and resample the distance driving factor layer and the slope driving factor layer to a preset resolution to obtain the spatial driving factor data; wherein, the first distance data, the second distance data, and the population raster data are human impact factors, the elevation data and the slope data are terrain factors, the soil raster data is a soil factor, and the climate raster data is a climate factor.

[0018] As an improvement to the above solution, obtaining natural data and inputting it into a dynamic vegetation model to obtain the leaf area coverage ratio of natural vegetation, specifically:

[0019] Obtain climate data and natural background data, input them into the dynamic vegetation model, and simulate to obtain the leaf area coverage ratios of different future vegetation functional types; among them, the climate data and natural background data are obtained through observation and simulation;

[0020] According to the natural vegetation corresponding to different vegetation functional types, classify and sum the leaf area coverage ratios of each of the different future vegetation functional types to obtain the leaf area coverage ratio of each natural vegetation.

[0021] As an improvement to the above solution, obtaining climate data and natural background data, inputting them into the dynamic vegetation model, and simulating to obtain the grid leaf area coverage ratios of different future vegetation functional types, specifically:

[0022] According to the preset physical process parameters and preset chemical process parameters, input them into the dynamic vegetation model to calculate the individual leaf area index;

[0023] According to the climate data and the natural background data, input them into the dynamic vegetation model to obtain the growth rate and death rate, and thus obtain the population density;

[0024] According to the population density, the individual leaf area index, and the canopy area, calculate the leaf area coverage rate FPC of different vegetation functional types, and the calculation formula is as follows:

[0025]

[0026] In the formula, CA represents the canopy area, k allom ,k rp are both constants, D represents the stem diameter, P represents the population density, and LAI ind represents the individual leaf area index.

[0027] As an improvement to the above solution, according to the leaf area coverage ratio, perform weighted calculation on the first development suitability data through a preset algorithm to obtain the second development suitability data, specifically:

[0028] According to the future period to be simulated, adjust the leaf area coverage ratio to the first development suitability data, and the preset adjustment rule is:

[0029] sp v =sp f +sp g +sp b

[0030] I f =(1 - ∈)S f + ∈FPC f ·Sp v

[0031] I g =(1 - ∈)S g + ∈FPC g ·sp v

[0032] I b =(1 - ∈)S b + ∈FPC b ·sp v

[0033] Wherein, sp f , sp g , sp b are preset values; S f , S g and S b are respectively the historical development suitability of forest land, grassland and bare land calculated by the FLUS model; FPC f , FPC g and FPC b are respectively the leaf area coverage ratios of forest land, grassland and bare land simulated by the dynamic vegetation model; I f , I g and I b are respectively the utilization development suitability of forest land, grassland and bare land, and ∈ is the influence weight of the dynamic vegetation model.

[0034] As an improvement of the above solution, the preset conversion rule is specifically:

[0035] Calculate the total conversion probability of the land type according to the second development suitability data, combined with the neighborhood effect, random factor, constraint factor and adaptive inertia coefficient;

[0036] Sample the target land use type according to the roulette selection method for the total conversion probability to obtain the land use type.

[0037] Correspondingly, the embodiment of the present invention also provides a future land use simulation device, including: a first calculation module, a second calculation module, a third calculation module and an iterative calculation module;

[0038] The first calculation module is used to obtain historical land use data and spatial driving factor data, and input them into the FLUS model to obtain the first development suitability data; among them, the first development suitability data includes historical development suitability of forest land, historical development suitability of grassland, and historical development suitability of bare land;

[0039] The second calculation module is used to obtain natural data and input it into the dynamic vegetation model to obtain the leaf area coverage ratio of natural vegetation; among them, the natural data includes climate data and natural background data, and the leaf area coverage ratio of the natural vegetation includes the leaf area coverage ratio of forest land, the leaf area coverage ratio of grassland, and the leaf area coverage ratio of bare land;

[0040] The third calculation module is used to perform weighted calculation on the first development suitability data according to the leaf area coverage ratio through a preset algorithm to obtain the second development suitability data;

[0041] The iterative calculation module is used to perform iterative conversion of the target land use type according to the second development suitability data by using a preset conversion rule to obtain the land use type corresponding to different grids, and stop the iteration and output the future land use demand until the number of the obtained land use types reaches a preset value.

[0042] As an improvement of the above solution, the first calculation module includes: a driving factor unit and a FLUS model unit;

[0043] The driving factor unit is used to collect historical land use data within a preset time, and collect human impact factors, terrain factors, soil factors, and climate factors, so as to obtain spatial driving factor data;

[0044] The FLUS model unit is used to obtain samples by using the method of stratified random sampling according to the historical land use data and spatial driving factor data, and input them into the FLUS model for training, testing, and verification, so as to obtain the first development suitability data.

[0045] As an improvement to the above solution, the collection of human impact factors, terrain factors, soil factors, and climate factors to obtain spatial driving factor data is specifically as follows: calculating the first distance data from each pixel point in a preset area to the city center and the second distance data from each pixel point to the road to obtain a distance driving factor layer; collecting elevation data and calculating the slope data of each pixel point in the area to obtain a slope driving factor layer; according to the population raster data, soil raster data, and climate raster data, projecting and resampling the distance driving factor layer and the slope driving factor layer to a preset resolution to obtain spatial driving factor data; where the first distance data, second distance data, and population raster data are human impact factors, elevation data and slope data are terrain factors, soil raster data is a soil factor, and climate raster data is a climate factor.

[0046] As an improvement to the above solution, the second calculation module includes: a dynamic vegetation model unit and a classification and summation unit;

[0047] The dynamic vegetation model unit is used to obtain climate data and natural background data, input them into the dynamic vegetation model, and simulate to obtain the leaf area coverage ratios of different future vegetation functional types; where the climate data and natural background data are obtained through observation and simulation;

[0048] The classification and summation unit is used to classify and sum the leaf area coverage ratios of each of the different future vegetation functional types according to the natural vegetation corresponding to the different vegetation functional types to obtain the leaf area coverage ratio of each natural vegetation.

[0049] As an improvement to the above solution, the obtaining of climate data and natural background data, inputting them into the dynamic vegetation model, and simulating to obtain the grid leaf area coverage ratios of different future vegetation functional types is specifically as follows: according to preset physical process parameters and preset chemical process parameters, inputting them into the dynamic vegetation model to calculate the individual leaf area index; according to the climate data and the natural background data, inputting them into the dynamic vegetation model to obtain the growth rate and death rate, thereby obtaining the population density; according to the population density, the individual leaf area index, and the canopy area, calculating the leaf area coverage rate FPC of different vegetation functional types, and the calculation formula is as follows:

[0050]

[0051] In the formula, CA represents the canopy area, k allom ,k rp are both constants, D represents the stem diameter. P represents the population density, and LAI ind represents the individual leaf area index.

[0052] As an improvement to the above solution, the third calculation module includes: an adjustment unit;

[0053] The adjustment unit is used to adjust the first development suitability data according to the future period to be simulated, and the preset adjustment rule is:

[0054] sp v = sp f + sp g + sp b

[0055] I f = (1 - ∈)S f + ∈FPC f ·sp v

[0056] I g = (1 - ∈)S g + ∈FPC g ·sp v

[0057] I b = (1 - ∈)S b + ∈FPC b ·sp v

[0058] In the formula, sp f , sp g , sp b are preset values; S f , S g and S b are the historical development suitability of forest land, grassland and bare land calculated by the FLUS model respectively; FPC f , FPC g and FPC b are the leaf area coverage ratios of forest land, grassland and bare land simulated by the dynamic vegetation model respectively; I f , I g and I b are the utilization development suitability of forest land, grassland and bare land respectively, and ∈ is the influence weight of the dynamic vegetation model.

[0059] As an improvement to the above solution, the iterative calculation module includes: a total conversion probability unit and a roulette selection method unit;

[0060] The total conversion probability unit is used to calculate the total conversion probability of land types according to the second development suitability data, in combination with neighborhood effects, random factors, constraint factors and adaptive inertia coefficients;

[0061] The roulette selection method unit is used to sample the total conversion probability for the target land use type according to the roulette selection method to obtain the land use type.

[0062] Correspondingly, an embodiment of the present invention further provides a computer terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a future land use simulation method as described in the present invention.

[0063] Correspondingly, an embodiment of the present invention further provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute a future land use simulation method as described in the present invention.

[0064] As can be seen from the above, the present invention has the following beneficial effects:

[0065] The present invention provides a future land use simulation method, device, storage medium, and terminal device. It uses a dynamic vegetation model to simulate the leaf area coverage ratio of natural resources under climate change, and combines the historical land use development suitability simulated by the FLUS model to obtain future land use demands. The present invention combines a dynamic vegetation model and the FLUS model to clearly simulate the impacts of human activities and natural climate on land use change, thereby improving the effectiveness of simulating future land use to provide more accurate data to help researchers conduct research in the field of geographic information science. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a flowchart of a future land use simulation method provided by an embodiment of the present invention;

[0067] Figure 2 is a structural diagram of a future land use simulation device provided by an embodiment of the present invention;

[0068] Figure 3 is a flowchart of a future land use simulation method provided by another embodiment of the present invention;

[0069] Figure 4 is a comparison diagram of the simulation accuracy of the present invention and that of a common method;

[0070] Figure 5 is a comparison diagram of the actual land use pattern in 2015, the land use pattern simulated by the present invention, and the land use pattern simulated by a common method;

[0071] Figure 6It is a comparison schematic diagram of the actual land use pattern in 2015, the land use pattern simulated by the present invention and the common method in a typical area;

[0072] Figure 7 It is a comparison schematic diagram of the proportion of the area change of forest land in the land use patterns simulated by the present invention and the common method from 2015 to 2100 under two climate scenarios;

[0073] Figure 8 It is a schematic diagram of the structure of a terminal device provided by an embodiment of the present invention. Specific embodiments

[0074] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0075] Embodiment 1

[0076] See Figure 1 , Figure 1 It is a schematic flowchart of a future land use simulation method provided by an embodiment of the present invention. As shown in Figure 1 , this embodiment includes steps 101 to 104, and the specific steps are as follows:

[0077] Step 101: Obtain land historical use data and spatial driving factor data, and input them into the FLUS model to obtain first development suitability data; wherein, the first development suitability data includes forest land historical development suitability, grassland historical development suitability, and bare land historical development suitability.

[0078] As an improvement of the above solution, collect land historical use data within a preset time, and collect human impact factors, terrain factors, soil factors, and climate factors to obtain spatial driving factor data; according to the land historical use data and spatial driving factor data, use the method of stratified random sampling to obtain samples, and input them into the FLUS model for training, testing, and verification to obtain first development suitability data.

[0079] As an improvement to the above solution, calculate the first distance data from each pixel point in the preset area to the city center and the second distance data from each pixel point to the road to obtain a distance driving factor layer; collect elevation data and calculate the slope data of each pixel point in the area to obtain a slope driving factor layer; project and resample the distance driving factor layer and the slope driving factor layer to a preset resolution according to population raster data, soil raster data, and climate raster data, so as to obtain spatial driving factor data; wherein, the first distance data, the second distance data, and the population raster data are human impact factors, the elevation data and the slope data are terrain factors, the soil raster data is a soil factor, and the climate raster data is a climate factor.

[0080] To better illustrate this step, the following example is given for illustration.

[0081] Spatial driving factor data includes: human impact factors, terrain factors, soil factors, and climate factors; among them, human impact factors include: population quantity, distance from the pixel point to the city, distance from the pixel point to the road, terrain factors include: elevation, slope, soil factors include: tillability, nutrient availability, salt content, root oxygen supply, and climate factors include: annual temperature range, temperature seasonality, annual average temperature, precipitation seasonality, annual average precipitation.

[0082] Use the MODIS historical land use data of 2001 and 2015, and use GIS software to calculate the distance from each pixel point to the city center and the distance from each pixel point to the road to generate a distance driving factor layer; collect elevation data and use GIS software to calculate the slope of each pixel point to generate a slope driving factor layer; collect population quantity data, soil factor data, and climate factor data, project and resample the distance driving factor layer and the slope driving factor layer to a 1km resolution, so as to obtain 14 driving factors.

[0083] According to the MODIS historical land use data of 2001 and the 14 driving factor data, 500,000 samples are obtained through stratified random sampling and input into the FLUS model for training, validation, and testing, so that the model outputs the first development suitability; among them, 60% of the samples are used as training samples, 20% of the samples are used as validation samples, and the remaining 20% of the samples are used as test samples.

[0084] As an improvement to the above solution, the FLUS model can adopt a random forest model.

[0085] Step 102: Obtain natural data and input it into the dynamic vegetation model to obtain the leaf area coverage ratio of natural vegetation; wherein, the natural data includes climate data and natural background data, and the leaf area coverage ratio of the natural vegetation includes the leaf area coverage ratio of forest land, the leaf area coverage ratio of grassland, and the leaf area coverage ratio of bare land.

[0086] As an improvement of the above solution, obtain climate data and natural background data, input them into the dynamic vegetation model, and simulate to obtain the leaf area coverage ratio of different future vegetation functional types; wherein, the climate data and natural background data are obtained through observation and simulation; according to the natural vegetation corresponding to different vegetation functional types, classify and sum the leaf area coverage ratio of each of the different future vegetation functional types to obtain the leaf area coverage ratio of each of the natural vegetation.

[0087] As an improvement of the above solution, according to the preset physical process parameters and preset chemical process parameters, input them into the dynamic vegetation model to calculate the individual leaf area index; according to the climate data and the natural background data, input them into the dynamic vegetation model to obtain the growth rate and death rate, so as to obtain the population density; according to the population density, the individual leaf area index, and the canopy area, calculate the leaf area coverage rate FPC of different vegetation functional types, and the calculation formula is as follows:

[0088]

[0089] In the formula, CA represents the canopy area, k allom ,k rp are both constants, D represents the stem diameter. P represents the population density, and LAI ind represents the individual leaf area index.

[0090] To better illustrate this step, the following example is given for illustration.

[0091] Collect natural data such as CO2 data, monthly average temperature, monthly average precipitation, solar radiation value, and fixed soil structure data, and input the natural data into the dynamic vegetation model for simulation: first calculate the growth rate and death rate of the vegetation to obtain the population density P, and then parameterize and model the biogeophysical process and biogeochemical process (such as: vegetation photosynthesis, etc.) to calculate the individual leaf area index LAI ind ,input the population density P and the individual leaf area index LAI ind into the calculation formula to obtain the network leaf area coverage ratio of different vegetation functional types under the future climate background, and the calculation formula is:

[0092]

[0093] In the formula, CA represents the canopy area, k allom , k rp are both constants, and D represents the stem diameter.

[0094] Among them, there are ten different vegetation function types, corresponding to three natural vegetations respectively, including: forest land (tropical broad-leaved evergreen forest, tropical broad-leaved deciduous forest, temperate coniferous evergreen forest, temperate broad-leaved evergreen forest, temperate broad-leaved deciduous forest, cold coniferous evergreen forest, cold coniferous deciduous forest), grassland (temperate grassland, tropical grassland) and bare land (bare land).

[0095] According to the leaf area coverage ratio of the ten vegetation function types, they are respectively summed up into the corresponding natural vegetations to obtain the leaf area coverage ratio of forest land, the leaf area coverage ratio of grassland and the leaf area coverage ratio of bare land; among them, the leaf area ratio is jointly determined by the physiological mechanism of the vegetation and future climate conditions, and can effectively reflect the development suitability of natural vegetation under the background of climate change.

[0096] As an improvement of the above solution, CO2 data, monthly average temperature, monthly average precipitation, solar radiation value and fixed soil structure data can be provided by historical climate scenarios, RCP-2.6 and RCP-8.5 climate scenario predictions (1900-2100), where the CO2 data, monthly average temperature, monthly average precipitation and solar radiation value are climate data, and the fixed soil structure data is natural background data.

[0097] Step 103: According to the leaf area coverage ratio, perform weighted calculation on the first development suitability data through a preset algorithm to obtain the second development suitability data;

[0098] As an improvement of the above solution, according to the future period to be simulated, adjust the leaf area coverage ratio to the first development suitability data, and the preset adjustment rule is:

[0099] sp v = sp f + sp g + sp b

[0100] I f =(1 - ∈)S f + ∈FPC f ·sp v

[0101] I g =(1 - ∈)S g + ∈FPC g ·sp v

[0102] I b=(1 - ∈)S b + ∈FPC b ·sp v

[0103] where sp f , sp g , and sp b are preset values; S f , S g , and S b are the historical development suitability of forest land, grassland, and bare land calculated by the FLUS model respectively; FPC f , FPC g , and FPC b are the leaf area coverage ratios of forest land, grassland, and bare land simulated by the dynamic vegetation model respectively; I f , I g , and I b are the utilization development suitability of forest land, grassland, and bare land respectively, and ∈ is the influence weight of the dynamic vegetation model.

[0104] To better illustrate this step, the following example is given for illustration.

[0105] Consider six categories of land use data, including grassland, forest land, urban, cultivated land, bare land, and water body. Among them, the utilization development suitability of land uses greatly affected by humans such as urban and cultivated land remains unchanged, and the water body is assumed not to change. And the calculated leaf area coverage ratio is used to perform weighted calculation on the first development suitability data through a preset algorithm to obtain the utilization development suitability of forest land, grassland, and bare land; among them, the utilization development suitability of forest land, grassland, and bare land is the second development suitability data.

[0106] Step 104: According to the second development suitability data, use the preset conversion rules to perform iterative conversion of the target land use type to obtain the land use types corresponding to different grids, and stop the iteration and output the future land use demand until the number of obtained land use types reaches the preset value.

[0107] As an improvement of the above solution, according to the second development suitability data, combined with the neighborhood effect, random factor, constraint factor, and adaptive inertia coefficient, calculate the total conversion probability of the land type; according to the roulette selection method, sample the total conversion probability for the target land use type to obtain the land use type.

[0108] As an improvement to the above solution, the land demand in 2015, the land demand in the RCP-2.6 scenario in 2100, and the land demand in the RCP-8.5 scenario in 2100 are used as the termination conditions for iteration.

[0109] As an improvement to the above solution, Figure 3 is a schematic flowchart of a future land use simulation method provided by another embodiment of the present invention. In Figure 3 , climate and natural background condition data are input into the dynamic vegetation model, and the leaf area ratios of 10 vegetation functional types are output, so as to obtain the leaf area ratios of forest, grassland, and bare land. At the same time, spatial driving factors and land use / cover data are obtained, samples are obtained using random forest, and the samples are input into the machine learning module in the FLUS model to obtain the historical development suitability of forest, grassland, and bare land. The historical development suitability of forest, grassland, and bare land and the leaf area ratios of forest, grassland, and bare land are input into the FLUS model as development suitability, and the total conversion probability is calculated by combining constraint factors, neighborhood effects, and random factors. The roulette selection method is used for the calculation result to determine the target land use type, so as to perform iterative calculation of the land use type. The future land use / cover demand (SSP-RCP scenario) is used as the iteration stop condition, so as to obtain the future land use / cover pattern.

[0110] For a better illustration of the effects of this embodiment, please refer to Figure 4 , Figure 5 , Figure 6 and Figure 7 .

[0111] Figure 4 is a comparison schematic diagram of the simulation accuracy of the present invention and that of the ordinary method. It can be seen from Figure 4 that the simulation accuracy of the present invention in terms of natural vegetation is greatly improved compared with the ordinary method; among them, the method of the present invention is DGVM-FLUS, and the ordinary methods are RF-FLUS and ANN-FLUS.

[0112] Figure 5 is a comparison schematic diagram of the actual land use pattern in 2015, the land use pattern simulated by the present invention, and the ordinary method. Figure 6 is a comparison schematic diagram of the actual land use pattern in 2015, the land use pattern simulated by the present invention, and the ordinary method in a typical area. It can be seen from Figure 5 and Figure 6 that the land use pattern simulated by the present invention is closer to the actual situation compared with the ordinary method; among them, the method of the present invention is DGVM-FLUS, and the ordinary methods are RF-FLUS and ANN-FLUS.

[0113] Figure 7It is a comparison schematic diagram of the area change ratio of forest land in two climate scenarios of the land use pattern simulated by the present invention and the ordinary method from 2015 to 2100. It can be seen that Figure 7 there are certain differences between the results simulated by the method of the present invention and the ordinary method, indicating that the present invention can better reflect the future land use change pattern; among them, the method of the present invention is DGVM-FLUS, and the ordinary method is RF-FLUS.

[0114] In this embodiment, the present method adjusts the leaf area coverage ratio output by the dynamic vegetation model as the land historical development suitability for the output of the FLUS model, and incorporates climate change as an influence on future land use change. Compared with the prior art, the accuracy of the future land use change simulation result of the present method is higher, so as to provide data for scientific researchers to process environmental problems and climate problems as a reference.

[0115] Embodiment 2

[0116] See Figure 2 , Figure 2 which is a structural schematic diagram of a future land use simulation device provided by an embodiment of the present invention, including: a first calculation module 201, a second calculation module 202, a third calculation module 203, and an iterative calculation module 204;

[0117] The first calculation module 201 is used to obtain land historical use data and spatial driving factor data, and input them into the FLUS model to obtain first development suitability data; wherein, the first development suitability data includes forest land historical development suitability, grassland historical development suitability, and bare land historical development suitability;

[0118] The second calculation module 202 is used to obtain natural data and input them into the dynamic vegetation model to obtain the leaf area coverage ratio of natural vegetation; wherein, the natural data includes climate data and natural background data, and the leaf area coverage ratio of the natural vegetation includes forest land leaf area coverage ratio, grassland leaf area coverage ratio, and bare land leaf area coverage ratio;

[0119] The third calculation module 203 is used to perform weighted calculation on the first development suitability data according to the leaf area coverage ratio through a preset algorithm to obtain second development suitability data;

[0120] The iterative calculation module 204 is used to perform iterative conversion of the target land use type according to the second development suitability data by using a preset conversion rule to obtain the land use type corresponding to different grids, and stop the iteration and output the future land use demand until the number of the obtained land use types reaches a preset value.

[0121] As an improvement of the above solution, the first calculation module 201 includes: a driving factor unit and a FLUS model unit;

[0122] The driving factor unit is used to collect historical land use data within a preset time, and collect human impact factors, terrain factors, soil factors, and climate factors, so as to obtain spatial driving factor data;

[0123] The FLUS model unit is used to obtain samples by using the method of stratified random sampling according to the historical land use data and spatial driving factor data, and input them into the FLUS model for training, testing, and verification, so as to obtain the first development suitability data.

[0124] As an improvement of the above solution, the collection of human impact factors, terrain factors, soil factors, and climate factors to obtain spatial driving factor data is specifically: calculating the first distance data from each pixel point in a preset area to the city center and the second distance data from each pixel point to the road to obtain a distance driving factor layer; collecting elevation data and calculating the slope data of each pixel point in the area to obtain a slope driving factor layer; according to the population raster data, soil raster data, and climate raster data, projecting and resampling the distance driving factor layer and the slope driving factor layer to a preset resolution to obtain spatial driving factor data; wherein, the first distance data, the second distance data, and the population raster data are human impact factors, the elevation data and the slope data are terrain factors, the soil raster data is a soil factor, and the climate raster data is a climate factor.

[0125] As an improvement of the above solution, the second calculation module 202 includes: a dynamic vegetation model unit and a classification and summation unit;

[0126] The dynamic vegetation model unit is used to obtain climate data and natural background data, input them into the dynamic vegetation model, and simulate the leaf area coverage ratio of different future vegetation functional types; wherein, the climate data and natural background data are obtained through observation and simulation;

[0127] The classification and summation unit is used to classify and sum the leaf area coverage ratio of each of the different future vegetation functional types according to the natural vegetation corresponding to different vegetation functional types to obtain the leaf area coverage ratio of each natural vegetation.

[0128] As an improvement to the above solution, the obtaining of climate data and natural background data, and inputting them into the dynamic vegetation model to simulate and obtain the grid leaf area coverage ratios of different future vegetation functional types is specifically as follows: According to the preset physical process parameters and preset chemical process parameters, input them into the dynamic vegetation model to calculate the individual leaf area index; According to the climate data and the natural background data, input them into the dynamic vegetation model to obtain the growth rate and death rate, thereby obtaining the population density; According to the population density, the individual leaf area index, and the canopy area, calculate the leaf area coverage rate FPC of different vegetation functional types, and the calculation formula is as follows:

[0129]

[0130] In the formula, CA represents the canopy area, k allom , k rp are both constants, D represents the stem diameter, P represents the population density, and LAI ind represents the individual leaf area index.

[0131] As an improvement to the above solution, the third calculation module 203 includes: an adjustment unit;

[0132] The adjustment unit is used to adjust the leaf area coverage ratio to the first development suitability data according to the future period to be simulated, and the preset adjustment rules are:

[0133] sp v = sp f + sp g + sp b

[0134] I f = (1 - ∈)S f + ∈FPC f ·sp v

[0135] I g = (1 - ∈)S g + ∈FPC g ·sp v

[0136] I b = (1 - ∈)S b + ∈FPC b ·sp v

[0137] In the formula, sp f , sp g , sp b are preset values; S f , S gand S b are the historical development suitability of forest land, grassland and bare land calculated by the FLUS model respectively; FPC f , FPC g and FPC b are the leaf area coverage ratios of forest land, grassland and bare land simulated by the dynamic vegetation model respectively; I f , I g and I b are the utilization development suitability of forest land, grassland and bare land respectively, and ∈ is the influence weight of the dynamic vegetation model.

[0138] As an improvement of the above solution, the iterative calculation module 204 includes: a total conversion probability unit and a roulette selection method unit;

[0139] The total conversion probability unit is used to calculate the total conversion probability of land types according to the second development suitability data, in combination with neighborhood effect, random factor, constraint factor and adaptive inertia coefficient;

[0140] The roulette selection method unit is used to sample the target land use type according to the roulette selection method for the total conversion probability to obtain the land use type.

[0141] In this embodiment, the first development suitability is calculated by the first calculation module, the leaf area coverage ratio of natural vegetation is calculated by the second calculation module, and the first development suitability and the leaf area coverage ratio are input into the third calculation module to calculate the second development suitability data, so that the iterative calculation module can perform iterative conversion of land use types according to the second development suitability data, thereby obtaining future land use demands. This embodiment adds the influence of climate change on land use simulation to the FLUS model, improving the accuracy of land use simulation.

[0142] Embodiment Three

[0143] See Figure 8 , Figure 8 which is a schematic structural diagram of a terminal device provided by an embodiment of the present invention.

[0144] A terminal device in this embodiment includes: a processor 801, a memory 802, and a computer program stored in the memory 802 and executable on the processor 801. When the processor 801 executes the computer program, it implements the steps in the above various future land use simulation methods in the embodiments, such as Figure 1 all the steps of the future land use simulation method shown. Or, when the processor executes the computer program, it implements the functions of each module in the above device embodiments, such as:Figure 2 All modules of the future land use simulation device shown.

[0145] In addition, an embodiment of the present invention further provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the future land use simulation method described in any of the above embodiments.

[0146] Those skilled in the art can understand that the schematic diagram is only an example of the terminal device, and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0147] The so-called processor 801 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor 801 is the control center of the terminal device, and connects various parts of the entire terminal device through various interfaces and lines.

[0148] The memory 802 can be used to store the computer program and / or modules. The processor 801 realizes various functions of the terminal device by running or executing the computer program and / or modules stored in the memory, and by calling the data stored in the memory 802. The memory 802 may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phone book, etc.), etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0149] Among them, if the modules / units integrated in the terminal device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described method embodiments of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0150] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described device can refer to the corresponding process in the foregoing method embodiments, and will not be elaborated herein.

[0151] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0152] The above is the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A future land use simulation method, characterized in that, Including: Obtain the historical land use data and spatial driving factor data, and input them into the FLUS model to obtain the first development suitability data; wherein, the first development suitability data includes the historical development suitability of forest land, the historical development suitability of grassland, and the historical development suitability of bare land; Obtain natural data and input it into the dynamic vegetation model to obtain the leaf area coverage ratio of natural vegetation; wherein, the natural data includes climate data and natural background data, and the leaf area coverage ratio of the natural vegetation includes the leaf area coverage ratio of forest land, the leaf area coverage ratio of grassland, and the leaf area coverage ratio of bare land; The obtaining of natural data and inputting it into the dynamic vegetation model to obtain the leaf area coverage ratio of natural vegetation is specifically: Obtain climate data and natural background data, input them into the dynamic vegetation model, and simulate the leaf area coverage ratio of different future vegetation functional types; wherein, the climate data and natural background data are obtained through observation and simulation; According to the natural vegetation corresponding to different vegetation functional types, classify and sum the leaf area coverage ratio of each of the future different vegetation functional types to obtain the leaf area coverage ratio of each of the natural vegetation; The obtaining of climate data and natural background data, inputting them into the dynamic vegetation model, and simulating the leaf area coverage ratio of different future vegetation functional types is specifically: According to the preset physical process parameters and preset chemical process parameters, input them into the dynamic vegetation model to calculate the individual leaf area index; According to the climate data and the natural background data, input them into the dynamic vegetation model to obtain the growth rate and death rate, so as to obtain the population density; According to the population density, the individual leaf area index, and the canopy area, calculate the leaf area coverage rate FPC of different vegetation functional types, and the calculation formula is as follows: where CA represents the canopy area, k allom , k rp are both constants, D represents the stem diameter, P represents the population density, and LAI ind represents the individual leaf area index; According to the leaf area coverage ratio, perform weighted calculation on the first development suitability data through a preset algorithm to obtain the second development suitability data; According to the second development suitability data, use the preset conversion rules to perform iterative conversion of the target land use type to obtain the land use type corresponding to different grids, and stop the iteration and output the future land use demand until the number of obtained land use types reaches the preset value.

2. The future land use simulation method according to claim 1, characterized in that The obtaining of the historical land use data and spatial driving factor data, and inputting them into the FLUS model to obtain the first development suitability data is specifically: Collect the historical land use data within a preset time, and collect human impact factors, terrain factors, soil factors, and climate factors to obtain the spatial driving factor data; According to the historical land use data and spatial driving factor data, use the method of stratified random sampling to obtain samples and input them into the FLUS model for training, testing, and verification to obtain the first development suitability data.

3. The future land use simulation method according to claim 2, characterized in that The collecting of human impact factors, terrain factors, soil factors, and climate factors to obtain the spatial driving factor data is specifically: Calculate the first distance data from each pixel point in the preset area to the city center and the second distance data from each pixel point to the road to obtain a distance driving factor layer; Collect elevation data and calculate the slope data of each pixel point in the area to obtain a slope driving factor layer; According to the population raster data, soil raster data, and climate raster data, project and resample the distance driving factor layer and the slope driving factor layer to a preset resolution to obtain spatial driving factor data; wherein, the first distance data, the second distance data, and the population raster data are human impact factors, the elevation data and the slope data are terrain factors, the soil raster data is a soil factor, and the climate raster data is a climate factor.

4. The future land use simulation method according to claim 1, characterized in that The weighted calculation of the first development suitability data is performed according to the leaf area coverage ratio through a preset algorithm to obtain the second development suitability data, specifically: According to the future period to be simulated, adjust the first development suitability data according to the leaf area coverage ratio, and the preset adjustment rule is: sp v = sp f + sp g + sp b I f =(1 - ∈)S f + ∈FPC f ·sp v I g =(1 - ∈)S g + ∈FPC g ·sp v I b =(1 - ∈)S b + ∈FPC b ·sp v wherein, sp f , sp g , sp b are preset values; S f , S g and S b are respectively the historical development suitability of forest land, grassland, and bare land calculated by the FLUS model; FPC f , FPC g and FPC b are respectively the leaf area coverage ratios of forest land, grassland, and bare land simulated by the dynamic vegetation model; I f , I g and I b are respectively the utilization development suitability of forest land, grassland, and bare land, and ∈ is the influence weight of the dynamic vegetation model.

5. The future land use simulation method according to claim 1, wherein The preset conversion rule is specifically: According to the second development suitability data, combine the neighborhood effect, random factor, constraint factor, and adaptive inertia coefficient to calculate the total conversion probability of the land type; According to the roulette selection method, sample the target land use type from the total conversion probability to obtain the land use type.

6. A future land use simulation device, characterized in that, Including: The first calculation module, the second calculation module, the third calculation module, and the iterative calculation module; The first calculation module is used to obtain the historical land use data and spatial driving factor data and input them into the FLUS model to obtain the first development suitability data; wherein, the first development suitability data includes the historical development suitability of forest land, the historical development suitability of grassland, and the historical development suitability of bare land; The second calculation module is used to obtain natural data and input it into the dynamic vegetation model to obtain the leaf area coverage ratio of natural vegetation; wherein, the natural data includes climate data and natural background data, and the leaf area coverage ratio of the natural vegetation includes the leaf area coverage ratio of forest land, the leaf area coverage ratio of grassland, and the leaf area coverage ratio of bare land; the obtaining of natural data and inputting it into the dynamic vegetation model to obtain the leaf area coverage ratio of natural vegetation is specifically: obtaining climate data and natural background data, inputting them into the dynamic vegetation model, and simulating to obtain the leaf area coverage ratio of different future vegetation functional types; wherein, the climate data and natural background data are obtained through observation and simulation; according to the natural vegetation corresponding to different vegetation functional types, classifying and summing the leaf area coverage ratio of each of the different future vegetation functional types to obtain the leaf area coverage ratio of each natural vegetation; the obtaining of climate data and natural background data, inputting them into the dynamic vegetation model, and simulating to obtain the leaf area coverage ratio of different future vegetation functional types is specifically: according to the preset physical process parameters and preset chemical process parameters, inputting them into the dynamic vegetation model to calculate the individual leaf area index; according to the climate data and the natural background data, inputting them into the dynamic vegetation model to obtain the growth rate and death rate, so as to obtain the population density; according to the population density, the individual leaf area index, and the canopy area, calculating to obtain the leaf area coverage rate FPC of different vegetation functional types, and the calculation formula is as follows: where CA represents the canopy area, k allom , k rp are both constants, D represents the stem diameter, P represents the population density, and LAI ind represents the individual leaf area index; The third calculation module is used to perform weighted calculation on the first development suitability data through a preset algorithm according to the leaf area coverage ratio to obtain the second development suitability data; The iterative calculation module is used to perform iterative conversion of the target land use type according to the second development suitability data by using a preset conversion rule to obtain the land use type corresponding to different grids, and stop the iteration and output the future land use demand until the number of obtained land use types reaches a preset value.

7. A computer terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a future land use simulation method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute a future land use simulation method according to any one of claims 1 to 5.

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